Environmental complexity is more important than mutation in driving the evolution of latent novel traits in E. coli

Recent experiments show that adaptive Darwinian evolution in one environment can lead to the emergence of multiple new traits that provide no immediate benefit in this environment. Such latent non-adaptive traits, however, can become adaptive in future environments. We do not know whether mutation or environment-driven selection is more important for the emergence of such traits. To find out, we evolve multiple wild-type and mutator E. coli populations under two mutation rates in simple (single antibiotic) environments and in complex (multi-antibiotic) environments. We then assay the viability of evolved populations in dozens of new environments and show that all populations become viable in multiple new environments different from those they had evolved in. The number of these new environments increases with environmental complexity but not with the mutation rate. Genome sequencing demonstrates the reason: Different environments affect pleiotropic mutations differently. Our experiments show that the selection pressure provided by an environment can be more important for the evolution of novel traits than the mutational supply experienced by a wild-type and a mutator strain of E. coli.

Supplementary note S1: Figure S1: Types of mutations in the two ancestral clones of the mutator strain. We sequenced the genomes of both our ancestral mutator clones and identified DNA variants in their genomes in which they differed from the previously reported sequence of the wild-type strain 1 . We observed 29 such mutations, 21 of which were present in both mutator clones. The first mutator clone harboured five additional mutations, while the second clone harboured three additional mutations. We classified the 29 mutations into four categories. Twenty-two of them were single nucleotide polymorphisms (SNPs), the most common type of mutations. One mutation was a small deletion (2 bp) in the coding region. Two small deletions (smaller than 50bp) and three SNPs occurred in intergenic regions. One mutations was a large insertion of 103bp. Source data are provided as a Source Data file.
We identified those mutations that were absent from the wild-type ancestral strain but present in at least one clone of our ancestral mutator strain. We observed 29 such mutations, 21 of which were present in both mutator clones ( Figure S1). One of these 21 mutations is the 103 bp insertion upstream of the gene mutL that is responsible for the ten-fold higher mutation rate of the mutator strain 2 .
Among the remaining twenty-eight mutations, we aimed to identify candidates to help explain why the mutator is viable in more phenotyping environments than the wild-type strain 1 . We identified five such candidates, four of which were shared by both mutator clones. The first was a single nucleotide change in the gene hns that encodes the nucleoid-associated DNA-binding protein H-NS. H-NS plays a major role in the organization of the bacterial chromosome and in the regulation of gene expression 3,4 . H-NS also regulates the expression of the marA gene, which encodes a global regulator of multidrug resistance in E. coli 3 . In addition, mutations in hns are known to affect drug resistance in E. coli 4 . The second mutation was a single nucleotide change in the gene ldtA, which encodes L,D-transpeptidase. β-lactams affect bacterial cell wall synthesis by binding to D,Dtranspeptidase, which cross-links the peptidoglycans that form the bacterial cell wall. Production of L,Dtranspeptidase can bypass the requirement for D,Dtranspeptidase, and confer resistance to a broad spectrum of β-lactam antibiotics 5,6 . The third mutation was a single nucleotide change in atoS, which encodes a sensory histidine kinase. Mutations in sensory histidine kinases can confer tolerance to the antibiotic vancomycin, as well as resistance to carbapenem antibiotics and heavy metals such as zinc 7,8 . The fourth shared mutation was a single nucleotide change in the regulatory region of the RNA chaperone Hfq, which regulates the multi-drug efflux pump AcrAB-TolC. Mutations in hfq can affect resistance to a wide variety of antimicrobials, such as acriflavine, benzalkonium, cefamandole, chloramphenicol, crystal violet, nalidixic acid, novobiocin, oxacillin and rhodamine 9 . The fifth candidate mutation, which occurred only in one ancestral mutator clone, was a single nucleotide change in the gene emrY, which encodes the membrane subunit of a tripartite efflux pump 10,11 . The EmrY/K efflux pump can confer resistance to a wide variety of antibiotics in E. coli, including ampicillin, tetracycline, penicillin, erythromycin, and chloramphenicol 10,11 . Overall, this analysis shows that at the beginning of experimental evolution, the mutator strain already harboured several mutations that may have increased its viability in our phenotyping environments.  Table S3: The table shows the IC90 for each antibiotic, the number of days of experimental evolution to which we subjected populations on each antibiotic, and the estimated 12 number of generations for evolution in each antibiotic for populations that had evolved in single antibiotic environments. These number of days and generations derive from a pilot experiment which had shown that bacteria differed in their tolerance to a daily increase in antibiotic dosage, depending on the antibiotic we used.
For instance, we could increase the concentration of ampicillin twice as fast as that of trimethoprim without population extinction. This difference among antibiotic environments led to different durations of experimental evolution on different single antibiotics. The previously reported evolution experiments of the wild-type strain 1 shared the starting concentrations, IC90 values, and number of days in experimental evolution with our current experiments on the mutator strain.   We performed experimental evolution in 3A1, 3A2 and 5A environments in two phases (Methods). The first phase was similar to evolution in simple evolution environments where we transferred 4 μl of culture volume every day, and increased the concentrations of all the antibiotics every second day.
We also applied the same criteria used for evolution in simple environments to identify populations with low growth (OD600 between 0.2 and 0.3, 20 μl inoculum volume) and extinction (OD600 < 0.2, revive 20 μl from the previous day's plate). We observed several extinctions near the end of phase I.
Specifically all eight wild-type populations evolving in the 3A2 environment went extinct on day 7. Two wild-type populations evolving in the 5A environment went extinct on day 9, while all eight populations went extinct on day 10. One mutator population evolving in the 5A environment went extinct on day 8, while all eight of the mutator populations went extinct in this environment on day 9. We revived these populations from the corresponding previous days' glycerol stocks but the repeated extinctions demanded a change of strategy for continuing experimental evolution. Overall, phase I lasted 12 days for wild-type and mutator populations evolving in the 3A1 environment; 10 days for wild-type populations evolving in the 3A2 environment; 13 days for wild-type populations evolving in the 5A environment; and 11 days for mutator populations evolving in the 3A2 and 5A environments.
Before beginning the second phase we evolved all wild-type and mutator populations from the end of phase I for four more days on the same antibiotic concentration as on the last day of phase I, in order to avoid extinctions. After these four days, we commenced phase II of experimental evolution, where we increased the concentration of only one antibiotic every day and increased the inoculum volume from the 4 μl of phase I to 100 μl, and from 20 μl to 200 μl for populations with low growth. These changes resulted in reduced selection pressure compared to phase I, and we observed no extinctions during the second phase of experimental evolution. On 7 out of ~135 days of phase II some of the populations showed low growth.
We observed two instances of contamination during this evolution experiment. The first instance occurred on the 33 th day of phase II. It affected one of the wild-type populations in the 5A environment, and two mutator populations from the 3A2 environment. The second instance affected one of these two mutator populations and occurred on day 37. In each instance we continued the experiment from the last glycerol stock archived before the contamination (Methods).
Phase II lasted 134 days for the wild-type populations evolved in the 3A2 and 5A environments, and 135 days for the wild-type populations evolved in the 3A1 environments, as well as for mutator populations evolved in the 3A1, 3A2, and 5A environments.

Supplementary note S3:
We estimated the approximate number of mutations experienced by our mutator populations evolved in the simple environments and our wild-type population evolved in the complex environments. For this purpose, we assumed that every population at the end of 24h growth had grown to ~2×10 8 cells, based on pilot experiments that used 2 ml LB broth in 24-well plates. In consequence, 4 µl of serialtransfer inoculum will then contain ~4×10 5 cells. The mutator strain has been estimated to experience ~22 mutations per 1000 cell generations 13 . Based on these numbers, ~4×10 6 mutational events will occur during 24 hours of growth. We evolved our mutator populations for an average of 17 of such 24 hour rounds in simple environments, which leads to an average of ~7×10 7 mutations.
Analogously, wild-type populations evolved in complex environments on average for 145 days to a final daily density of ~2×10 8 cells, a serial-transfer inoculum of ~100×10 5 cells, and a mutation rate of ~1 mutation per 1000 cell generations, leading to an expected average of ~3×10 7 mutations per population.
In sum, the number of mutations experienced by mutator populations evolved in simple environments and wild-type populations evolved in complex environment is of the same order of magnitude.   Identifying other candidate mutations for cellular targets of antibiotics and for multi-drug resistance reliably was infeasible in these mutator clones, because they accumulated hundreds of mutations (Table S7).    S10: Antimicrobials on which viability evolved belonged to a broad diversity of drugclasses whose mechanism of action generally differs from that of the antibiotic in the evolution environment. Five of the seven drug-classes used in the classification of the mechanism of action share this mechanism with the five antibiotics used during our evolution experiment.
Antimicrobials with a mechanism of action different from these five antibiotics are in the class 'other', while antimicrobials with an unknown mechanism of action are grouped in the class 'unknown'.
Antimicrobials that belong to a drug-class different than the antimicrobial in the evolution environment are marked with an '*'.
Evolution environment Strain Antimicrobial on which viability evolved